Currently, usage of automated or semi-automated machines or apparatuses (“robots”) (e.g., x-y tables, articulated robots, beam robotics, single axis, multi-axis, motor driven machinery, etc.), referred to as “robots,” can increase productivity and performance, and save costs in many environments. In this regard, robots typically can perform tasks or applications with greater accuracy, precision and consistency than manual or non-automated approaches. These increases in accuracy, precision and consistency may result in quality improvements. Robots typically have the ability to interact with one or more tangible objects and may be programmed to perform specific tasks or actions. Some modern robots may be fixed in place or capable of moving around in their environment to access areas of interest and interact with tangible objects to perform automated tasks. Many of the areas of interest that a robot accesses may be situated in a tightly controlled environment. For example, a robot (e.g., an automation arm) utilized in an automotive factory may need to access one or more locations for performing a weld or installing components of a vehicle in a tightly controlled area such as, for example, an assembly line.
In many instances, robots will need to be taught the manner in which to access areas of interest (also referred to herein as a target(s)) to perform one or more tasks in order to complete a work cycle for an environment. Currently, teaching a robot a manner in which to access one or more targets typically involves an operator manually guiding the robot to particular locations and recording values associated with the locations. For example, in an automotive environment, the operator may need to utilize a control pad to manually input data to guide the robot to a weld location and utilize a device to record data indicating the weld location such that the robot may know the location in which to perform a weld on a vehicle at some future time. Additionally, the operator may need to utilize the control pad to manually input data to guide the robot to a paint location and may utilize a device to record data indicating the paint location such that the robot may know the location to access in order to paint a portion of a vehicle at some future time. Also, the operator may need to utilize the control pad to manually guide the robot to an assembly location and utilize a device to record data indicating the assembly location such that the robot may know the location to access in the future in order to assemble a component(s) of a vehicle. This process may continue until a work cycle for the robot is completed.
One drawback of this approach involving the operator manually inputting data to a control pad to guide the robot to targets is that it can be a laborious, burdensome and time consuming process, since the operator may have to teach the robot the manner in which to access multiple targets for completion of a work cycle. For example, in some environments, a robot may be assigned 21 different targets at which to perform one or more different tasks, and teaching a robot these positions may take a large amount of time.
Additionally, it should be pointed out that guiding a robot to targets can require a high level of accuracy to avoid inadvertent collisions with other structures. At present, an operator may rely on his/her sight to visually guide the robot to targets with the aim of avoiding collisions. However, relying on the sight of the operator may be imprecise, and there may be instances in which the operator may be unable to manually guide the robot to targets without the robot colliding with other structures such as, for example, in areas of high congestion. As such, expensive equipment may be damaged.
As described above, in some instances, an operator may utilize a control pad to manually input data in order to guide a robot to targets and the control pad may receive feedback from a sensor (e.g., a switch) indicating that the locations corresponding to targets are nearby. While using the sensors to provide feedback to indicate when the location of a target is nearby may be of some assistance to the operator, the feedback from the sensors still may be ineffective in instances in which a high level of precision is required. For instance, in tight or congested areas the sensors may not provide the level of accuracy needed to identify the location of a target. As such, a robot still may run the risk of colliding with structures. Additionally, providing sensor feedback to a control pad to indicate to the operator whether a location of a target is nearby typically does not solve the problem associated with manually teaching the robot the locations of the targets, since the operator may still need to utilize the control pad to manually guide the robot to locations corresponding to targets.
In view of the foregoing drawbacks, it may be desirable to provide an efficient and reliable mechanism in which to teach a robot the location of targets for performing tasks. Additionally, in view of the above drawbacks, it may be beneficial to provide a mechanism for minimizing the likelihood of the robot colliding with one or more structures in an automated environment.